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Related Experiment Video

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Robotic Sensing and Stimuli Provision for Guided Plant Growth

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Scalable time-constrained planning of multi-robot systems.

Alexandros Nikou¹, Shahab Heshmati-Alamdari¹, Dimos V Dimarogonas¹

¹Division of Decision and Control, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, 100 44 Stockholm, Sweden.

Autonomous Robots

|October 22, 2020

Summary

This study introduces a scalable method for multi-robot planning under uncertainty. It enables decentralized control for robots to achieve complex tasks, ensuring safety and efficiency in real-world applications.

Keywords:

Abstractions Cooperative control Decentralized control Metric interval temporal logic (MITL)Multi-robot systems Nonlinear model predictive control (NMPC)Robust control

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Area of Science:

Robotics
Control Systems
Artificial Intelligence

Background:

Multi-robot systems require robust planning for complex tasks.
Decentralized control is crucial for scalability and local information usage.
Uncertainty and temporal logic specifications pose significant challenges.

Purpose of the Study:

To develop a scalable, time-constrained planning procedure for uncertain nonlinear multi-robot systems.
To design decentralized and robust control laws for individual robot specifications.
To ensure collision avoidance and meet transient constraints.

Main Methods:

Utilizes decentralized finite-horizon optimal control (DFHOCP) for online nominal control.
Employs an offline additive state feedback law for trajectory robustness.
Models robot transitions using individual weighted transition systems (WTS).
Scalability achieved without product computation among WTS.

Main Results:

The proposed decentralized controllers ensure robots meet metric interval temporal logic (MITL) specifications.
Collision avoidance and desired transient behaviors are guaranteed.
The framework demonstrates scalability for a large number of robots (N).

Conclusions:

The developed framework is scalable and effective for time-constrained planning in uncertain multi-robot systems.
Experimental validation confirms the approach's promise for robotic and industrial applications.
Decentralized control using local information is feasible for complex multi-robot coordination.